The present disclosure relates to a method and apparatus for automatically adjusting a license plate that is detected in a captured image so that the license identification number can be analyzed and identified. It is appreciated that the present exemplary embodiments are also amendable to other like applications.
An automatic license plate recognition (ALPR) system is a vehicular surveillance system that captures images of moving or parked vehicles using a still or video camera. The system locates a license plate in the image and uses optical character recognition (OCR) to determine the license plate number. The ALPR system often functions as a core module for an intelligent transportation infrastructure system as its many uses can include monitoring traffic flow, enforcing traffic laws, and tracking criminal suspects.
One challenge to the ALPR technology is its robustness to geometric variations. The position of a camera, such as the incidence angle between the camera and a target capture area, and parameters of the camera can affect the quality of the captured image due to distortion. The license plate in the image may be affected by a different scale, rotation angle or other geometric distortion. While most ALPR cameras are carefully positioned to minimize perspective distortion, severe shearing is still a common result.
One possible solution for distortion is to design an ALPR algorithm that is invariant to geometric changes. However, such a solution is often hindered by increased computational costs and other implications, such as, for example, a reduced accuracy of detection. Generally, the inherent computational complexity, and the computational cost, of a system are proportional to the product of the search range for each geometric parameter.
For many ALPR applications, such as monitoring toll gates on toll roads, the geometric configuration is usually fixed, or has limited variation, for each camera. Therefore, conventional ALPR systems rely on user-input to reduce a search range. For example, the system may ask a user to specify anticipated minimum and maximum heights of the license plates in the input images. The system uses the information received as user-inputs for determining scale. The system may also receive an anticipated maximum plate angle as a user-input.
There are several disadvantages and limitations for the practice of receiving user-inputs for analyzing purposes. The users must possess a certain image processing knowledge for measuring the plate height and rotation angle contained in the images while certain parameters, such as shearing, are not readily obtained by the users. This practice also requires a significant amount of work be performed by users. Furthermore, the manual inputs that are provided by the users may be incorrect or inaccurate. The system may therefore rely on inaccurate measurements for performing its analysis. Therefore, a need exists for a method and an apparatus that automatically adjusts the images of license plates that are detected in captured images.
Another disadvantage with the conventional practice is that the actual geometrical and camera parameters may drift over time. For example, the camera may tilt from its original position. Accordingly, a method and apparatus is needed which automatically calibrates parameters of an ALPR camera to compensate for displacement over time. In this manner, the system is adapted to automatically estimate, in advance, the amount of shearing to expect from a captured image before a vehicle arrives at the image capture device. Accordingly, a system is needed that is adapted to estimate shearing patterns is an image capture device.